Cost⁻Benefit Optimization of Structural Health Monitoring Sensor Networks.

Area of Science:

• Engineering
• Information Theory
• Computational Mechanics

Background:

• Structural health monitoring (SHM) is crucial for assessing mechanical system integrity.
• Existing methods for sensor network design lack cost-benefit optimization.
• Quantifying the effectiveness of SHM systems requires accounting for experimental uncertainties.

Purpose of the Study:

• To develop a cost-benefit optimization method for sensor network design in SHM.
• To define sensor density, type, and positioning for optimal information gain.
• To compare two distinct optimization strategies for sensor deployment.

Main Methods:

• Utilizing information theory, specifically Shannon information gain, to quantify SHM system effectiveness.
• Employing a framework with surrogate models (polynomial chaos expansion), model order reduction (principal component analysis), and stochastic optimization.
• Developing two optimization strategies: maximizing information gain under constraints and maximizing the information-cost ratio.

Main Results:

• A computational framework was developed to efficiently evaluate the cost-benefit of sensor networks.
• The proposed methods allow for the determination of optimal sensor network configurations.
• Application to the Pirelli tower demonstrates the framework's efficacy in large-scale structural problems.

Conclusions:

• The presented method provides a robust approach for optimizing sensor network design in SHM.
• Effective sensor network deployment can be achieved by balancing information gain with deployment costs.
• This framework enhances the efficiency and reliability of structural integrity assessments.